Fingerprint sensing and matching is a reliable and widely used technique for personal identification or verification. In particular, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference or enrolled fingerprints already in a database to determine proper identification of a person, such as for verification purposes.
Traditional approaches for fingerprint matching generally rely on minutia, which are point features corresponding to ridge ends and bifurcations. However, minutia-based matchers may have certain drawbacks. First, minutia extraction may be difficult in images of poor quality. Second, a minimum fingerprint area may be needed to extract a reasonable number of minutiae. Thus, minutia-based matchers may be unsuitable in applications where poor quality images or small sensors are involved. Using fingerprint pattern features, instead of minutiae, for matching may potentially mitigate both of these drawbacks. Examples of fingerprint pattern features are image pixel values, ridge flow, and ridge frequency.
Ridge flow information has been used in a variety of stages in fingerprint recognition. It is commonly extracted through tessellating the fingerprint image into small square blocks or cells and estimating the dominant ridge flow within each block. The resulting map is referred to as the ridge flow map.
Despite the existence of such ridge flow techniques, they may not provide desired matching speed for some implementations.